



"""simple docstring"""






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


lowerCAmelCase__              =							logging.getLogger(__name__)






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		List[str]		,   __lowerCAmelCase	:		Optional[int]		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Any		,   __lowerCAmelCase	:		List[str]=None						):






         """simple docstring"""






         super().__init__(
             __lowerCAmelCase		,   question_encoder_tokenizer=__lowerCAmelCase		,   generator_tokenizer=__lowerCAmelCase		,   index=__lowerCAmelCase		,   init_retrieval=__lowerCAmelCase		,   )
         _lowerCamelCase		:							List[str]            =					None




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         logger.info('''initializing retrieval'''						)

         # initializing a separate process group for retrieval as the default
         # nccl backend doesn't support gather/scatter operations while gloo
         # is too slow to replace nccl for the core gpu communication
         if dist.is_initialized():
                logger.info('''dist initialized'''						)
                # needs to be set manually
                _lowerCamelCase		:							str            =					self._infer_socket_ifname()
                # avoid clash with the NCCL port
                _lowerCamelCase		:							Optional[int]            =					str(distributed_port + 1						)
                _lowerCamelCase		:							Dict            =					dist.new_group(ranks=__lowerCAmelCase		,   backend='''gloo'''						)

         # initialize retriever only on the main worker
         if not dist.is_initialized() or self._is_main():
                logger.info('''dist not initialized / main'''						)
                self.index.init_index()

         # all processes wait untill the retriever is initialized by the main process
         if dist.is_initialized():
                torch.distributed.barrier(group=self.process_group						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         return dist.get_rank(group=self.process_group						) == 0




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		List[Any]		,   __lowerCAmelCase	:		Dict=torch.floataa						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					torch.empty(__lowerCAmelCase		,   dtype=__lowerCAmelCase						)
         dist.scatter(__lowerCAmelCase		,   src=0		,   scatter_list=__lowerCAmelCase		,   group=self.process_group						)
         return target_tensor




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					psutil.net_if_addrs()
         # a hacky way to deal with varying network interface names
         _lowerCamelCase		:							int            =					next((addr for addr in addrs if addr.startswith('''e'''						))		,   __lowerCAmelCase						)
         return ifname




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         if not dist.is_initialized():
                _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					self._main_retrieve(__lowerCAmelCase		,   __lowerCAmelCase						)
                return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase						)

         # distributed training
         _lowerCamelCase		:							Tuple            =					dist.get_world_size(group=self.process_group						)

         # gather logic
         _lowerCamelCase		:							int            =					None
         if self._is_main():
                _lowerCamelCase		:							Dict            =					[torch.empty(question_hidden_states.shape		,   dtype=torch.floataa						) for _ in range(__lowerCAmelCase						)]
         dist.gather(torch.tensor(__lowerCAmelCase						)		,   dst=0		,   gather_list=__lowerCAmelCase		,   group=self.process_group						)

         # scatter logic
         _lowerCamelCase		:							List[Any]            =					question_hidden_states.shape[0]
         _lowerCamelCase		:							Optional[int]            =					[]
         _lowerCamelCase		:							Any            =					[]
         if self._is_main():
                assert len(__lowerCAmelCase						) == world_size
                _lowerCamelCase     ,       _lowerCamelCase		:							Any            =					self._main_retrieve(torch.cat(__lowerCAmelCase						).numpy()		,   __lowerCAmelCase						)
                _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					torch.tensor(__lowerCAmelCase						), torch.tensor(__lowerCAmelCase						)
                _lowerCamelCase		:							Union[str, Any]            =					self._chunk_tensor(__lowerCAmelCase		,   __lowerCAmelCase						)
                _lowerCamelCase		:							Dict            =					self._chunk_tensor(__lowerCAmelCase		,   __lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					self._scattered(__lowerCAmelCase		,   [n_queries, n_docs]		,   target_type=torch.intaa						)
         _lowerCamelCase		:							int            =					self._scattered(__lowerCAmelCase		,   [n_queries, n_docs, question_hidden_states.shape[1]]						)

         return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCAmelCase						)




"""simple docstring"""


class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		name
              UpperCamelCase       			=		value
              UpperCamelCase       			=		weight






       def __repr__(	self			:				int      )->	Any:







              """simple docstring"""
              return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              return self.value






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	int:







              """simple docstring"""
              return self.name






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[Any]:







              """simple docstring"""
              return self.weight






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Union[str, Any]:







              """simple docstring"""
              return self.value / self.weight





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		[]
       for i in range(len(UpperCAmelCase_					)					):
              menu.append(Things(name[i]		,   value[i]		,   weight[i]					)					)
       return menu



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		sorted(UpperCAmelCase_		,   key=UpperCAmelCase_		,   reverse=UpperCAmelCase_					)
       UpperCamelCase       			=		[]
       UpperCamelCase			,							UpperCamelCase       			=		0.0, 0.0
       for i in range(len(UpperCAmelCase_					)					):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]					)
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)



def 						lowerCamelCase__      (				)->				Dict:







       """simple docstring"""


if __name__ == "__main__":
      import doctest

      doctest.testmod()





